Fuel poverty continues to challenge the social housing sector. Currently, around 11.4 percent of households in England – equivalent to approximately 2.8 million people – are living in fuel poverty under the Low Income Low Energy Efficiency metric.
Within social housing, 75.9 percent of homes were rated EPC bands A to C, but the remaining 24.1 percent of homes still fell into bands D to G. More than half of those properties, around 54.5 percent, were classed as fuel poor.
Rising energy costs, ageing buildings, and the national drive toward net zero have created a clear need for smarter, data-led management of housing stock.
The Social Housing Decarbonisation Fund (SHDF) is a key part of the national response. It supports landlords to improve energy performance, cut emissions, and reduce costs for tenants. Yet many landlords face the same challenge once upgrades are installed: how to prove that the improvements are working?
That’s where ZapIQ delivers value.
ZapIQ combines advanced sensors with artificial intelligence to provide continuous insight into how homes perform. It measures temperature, humidity, ventilation, and heat retention across multiple rooms, producing a live picture of the indoor environment. The AI engine analyses this data, detecting trends that point to heat loss, underheating, or mould risk. This enables landlords to target resources efficiently and protect tenants from fuel poverty.
ZapIQ turns complex building data into clear, actionable intelligence by highlighting homes that lose heat fastest or take longest to warm up, allowing landlords to focus retrofit investment where it delivers the greatest impact on cost and carbon reduction. AI models learn from every data point, improving predictions and helping organisations make faster, evidence-based decisions
The system also supports long-term performance assurance. SHDF funding requires clear demonstration of benefits, yet post-retrofit monitoring often remains limited. ZapIQ closes this gap by tracking outcomes in real time and alerting landlords to declining performance before it affects residents. This strengthens compliance, reduces waste, and ensures that funding translates into measurable results.
Protecting tenants and safeguarding assets are central to this approach. Cold, damp homes harm health and increase repair costs. ZapIQ’s predictive monitoring identifies environmental risks early, supporting proactive maintenance and improving living conditions. For landlords, this reduces complaints, strengthens trust, and enhances the overall quality of housing stock.
ZapIQ integrates intelligence directly into housing management.
It enables asset, sustainability, and housing teams to work from shared data, moving from reactive management to predictive planning. The result is a continuous feedback loop that aligns operational performance with strategic decarbonisation objectives.
With SHDF waves expanding, and regulatory expectations rising, housing providers need a reliable way to evidence performance and protect tenants.
ZapIQ’s AI-driven insight delivers that capability, turning measurement into action, data into strategy, and homes into healthier, more efficient places to live.
Sources
- Department for Energy Security and Net Zero (DESNZ). Annual Fuel Poverty Statistics in England, 2025 (2023 data). February 2025.
- Department for Energy Security and Net Zero (DESNZ). Sub-Regional Fuel Poverty Data, 2025 (2023 data). May 2025.
